Electrocardiography (ECG) in various medical environments (e.g. resting, monitoring) is the most commonly employed diagnostic modality in clinical cardiology and an essential tool for epidemiological studies and clinical trials. In general, ECG signal processing algorithms are designed based upon clinical experience and engineering principles. This signal processing methodology is used for interactive analysis of hours of electrocardio-graphic data in order to refine diagnostic accuracy and/or improve prognostic power. Currently a package of signal processing algorithms at DCRT has been ported to systems at National Children's Medical Center in Washington, D.C. This package has been used to analyze five hour tilt-table studies on young patients who have a history of syncope. It extracts variability measures of heart period and blood pressure in order to characterize autonomic dysfunction in these patients. The package has been updated to produce statistics allowing the cardiologist to track patients' responses to administration of an -sympathomimetic agent and to intravascular volume supplementation. These results form a basis for determining the clinical management of these patients. A collaboration was initiated with an endocrinologist at the U.S. Uniform Health Services University and a pediatric cardiologist at Walter Reed Hospital concerning the use of heart period variability in tilt-table studies to reveal subclinical autonomic neuropathy in young diabetic patients. It is believed that this tool could be used to correlate the onset of neuropathy and its severity with the duration of diabetes and the degree to which the patient controls hyperglycemia. A research protocol has been designed which now awaits approval by their research advisory boards. A modification of the analysis package will be needed for this collaboration.